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ArxivFusedWithPatents

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Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 118, in _split_generators
                  self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
                                                                           ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 2392, in read_schema
                  file = ParquetFile(
                         ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 328, in __init__
                  self.reader.open(
                File "pyarrow/_parquet.pyx", line 1656, in pyarrow._parquet.ParquetReader.open
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Fused Patent + arXiv Technical Clustering Dataset (Deterministic, Quality-Gated)

Overview

This dataset is the output of a zero-touch technical clustering pipeline built over a fused corpus of patent text and arXiv-style research text.

The pipeline is fully deterministic from ingest through release and is designed to run end-to-end without manual curation or mid-run intervention. All artifacts, cluster assignments, and release decisions are derived from the same run.

This is not a curated dataset. It is a large-scale fused technical corpus that has been deterministically analyzed and quality-gated to isolate the portion that behaves like a semantic clustering dataset.

Key Stats

  • Total labeled rows: 9,063,272
  • Raw clusters (pre-filter): 422
  • Release clusters (post-filter): 147
  • Retained rows: 3,881,329
  • Retention rate: 42.82%
  • Shards: 91 (labels / embeddings / chunks)
  • Size: ~20+ GB compressed

Pipeline Summary

The dataset was produced by a staged, resumable pipeline with Postgres acting as a control plane.

Core stages

  • Ingest and normalize fused patent + arXiv text
  • Chunk-level embedding
  • Embedding clustering
  • Shard-level processing with persistent state
  • Reducer-tree merge into global clusters
  • Global assignment + BM25 artifact generation
  • Deterministic inspection and release gating

System Design

The pipeline is built to operate under real constraints (long runtimes, memory pressure, interruptions), not ideal notebook conditions.

Control plane (Postgres)

  • Task leasing and discovery
  • Heartbeats and worker liveness
  • Stage state tracking (not-ready / running / done / failed)
  • Reducer-tree coordination and staged unblocking

Failure-aware execution

  • Distinguishes between:

    • true OOM
    • bad allocation
    • killed process
    • general memory pressure

  • Descending batch ladder (deterministic step-down on failure)

  • Proactive downshifting based on resource pressure

  • Resumable state across interruptions

Reducer-tree merge

  • Progressive level-by-level reduction
  • Final stage unblocked only after upstream completion
  • Prevents global merge bottlenecks
  • Avoids downstream fan-out gaps

Deterministic Quality Gating

The raw clustering output was not treated as valid by default.

A full deterministic inspection pass across all 422 clusters produced:

  • 147 coherent clusters
  • 107 mixed clusters
  • 168 metadata-heavy clusters

Filtering decision

For the release dataset:

  • Kept: coherent clusters only
  • Dropped: mixed + metadata-heavy clusters

This was done without:

  • re-embedding
  • hand labeling
  • manual cluster editing
  • modifying the original run

All decisions are reproducible from pipeline outputs.

Metadata Leakage

A large portion of clusters were dominated by ingestion or wrapper fields such as:

  • source_file
  • record_hash
  • raw_meta_json
  • authors_parsed
  • published_date
  • similar structural tokens

These are not errors in the source data, but they degrade semantic clustering if left unfiltered.

Explicit detection and removal of these clusters is a core part of the release process.

Dataset Structure

The release package includes filtered artifacts aligned to the retained clusters:

  • labels/ — cluster assignments
  • chunks/ — source text chunks
  • embeddings/ — embedding vectors
  • microclusters/ — original microcluster outputs (for provenance)
  • global/ — cluster summaries, BM25 artifacts, reference data

All components are consistent with the same filtered subset.

What This Dataset Is

  • A deterministically derived technical clustering dataset
  • A fused patent + research corpus with broad technical coverage
  • A quality-gated subset of a larger clustering run
  • A reproducible artifact tied to a single pipeline execution

What This Dataset Is Not

  • Not manually curated
  • Not hand-labeled
  • Not cleaned via ad-hoc scripts
  • Not a “perfect” semantic dataset
  • Not independent from its pipeline (the pipeline defines it)

Example Cluster Themes

Cluster naming was derived deterministically from top terms. Example themes include:

  • wireless communication systems
  • semiconductor substrates and layers
  • chemical compounds and formulations
  • neural networks and data processing
  • vehicle control systems
  • signal processing and circuits

Intended Use

  • Retrieval / RAG experiments
  • Technical topic clustering
  • Cross-domain similarity analysis
  • Large-scale embedding evaluation
  • Downstream filtering / refinement pipelines

Notes

This dataset represents the release-grade subset of the full run. The original unfiltered output (422 clusters) is intentionally not presented as the primary artifact.
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